基于改进T-S模型的模糊辨识算法及其应用

热工过程往往具有非线性和不确定性,用传统的描述热工过程动态数学模型的方法(如传统函数等)难以建立非线性模型,从而难于精确表达热工过程及实施整体优化控制。该文提出了一种基于改进T-S模型的热工系统模糊辨识方法。采用启发性知识与复合非线性优化方法相结合的综合方法求解出模糊模型的结构,然后通过基于熵的聚类和竞争学习算法对热工过程的输入数据空间的划分,在此基础上利用加权递推最小二乘法(WRLSA)建立热工过程的T-S模型。仿真结果表明基于改进T-S模型的非线性模糊模型,不仅能精确地描述过程的非线性,而且算法简单、快速。

Fuzzy Identification Based on Improved T-S Fuzzy Model and Its Application for Thermal Process

Thermal processes generally contain nonlinearity and randomicity, and it is difficult to build the nonlinear models by the traditional method such as the transfer function, so the whole optimal control for thermal processes is impossible. A kind of method of fuzzy identification was proposed based on improved T-S model. The heuristic information and the multiplex nonlinear optimization were combined to configure the structure of fuzzy model, and the input data space was partitioned into some local regions based on entropy clusting and competitive learning algorithm, and then the T-S model for thermal process was built with weighted recursive least-square algorithm (WRLSA). The simulation results show that the proposed improved T-S model can describe the non-linearity of processes accurately, and the relevant algorithms are very simple and fast.